{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tsfresh import extract_features\n",
    "from tsfresh.utilities.dataframe_functions import make_forecasting_frame\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Collect the data for the google stock "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>13.87</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.97</td>\n",
       "      <td>38525811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>13.97</td>\n",
       "      <td>14.00</td>\n",
       "      <td>13.51</td>\n",
       "      <td>13.72</td>\n",
       "      <td>50267536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>13.56</td>\n",
       "      <td>13.56</td>\n",
       "      <td>13.05</td>\n",
       "      <td>13.11</td>\n",
       "      <td>61285453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>12.90</td>\n",
       "      <td>13.04</td>\n",
       "      <td>12.60</td>\n",
       "      <td>12.70</td>\n",
       "      <td>57846688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>13.05</td>\n",
       "      <td>13.10</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.54</td>\n",
       "      <td>46199413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume\n",
       "Date                                            \n",
       "2016-01-04  13.87  14.00  13.75  13.97  38525811\n",
       "2016-01-05  13.97  14.00  13.51  13.72  50267536\n",
       "2016-01-06  13.56  13.56  13.05  13.11  61285453\n",
       "2016-01-07  12.90  13.04  12.60  12.70  57846688\n",
       "2016-01-08  13.05  13.10  12.50  12.54  46199413"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = datetime.datetime(2016, 1, 1)\n",
    "end = datetime.datetime(2017, 1, 1)\n",
    "x = web.DataReader(\"F\", 'google', start, end)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2016-01-04 to 2016-12-30\n",
      "Data columns (total 5 columns):\n",
      "Open      252 non-null float64\n",
      "High      252 non-null float64\n",
      "Low       252 non-null float64\n",
      "Close     252 non-null float64\n",
      "Volume    252 non-null int64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 11.8 KB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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TraAta7MyDFXFPo6ZUkoknmZna5QFZXOJajE2B7ePeNuFyBUhLYIr6QNATVtDHrdttNZi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KJiQgLP9hbHqkFG0SiUQikUimjxEWoi3X30+2R3gVGOUTb8gPZ0plO4RYy3S0j/u+oqpUfPAO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\nFFPRJiIiIiIiEsX+P5XwQRN885C5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e30610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.drop(\"Volume\", axis=1).plot(figsize=(15, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So, we loaded the google stock for one year. Now, we want to predict the High column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Create forecasting frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_shift, y = make_forecasting_frame(x[\"High\"], kind=\"price\", max_timeshift=20, rolling_direction=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4329</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4080</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4330</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>price</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time  value         id   kind\n",
       "4579 2016-01-04   14.0 2016-01-05  price\n",
       "4329 2016-01-04   14.0 2016-01-06  price\n",
       "4580 2016-01-05   14.0 2016-01-06  price\n",
       "4080 2016-01-04   14.0 2016-01-07  price\n",
       "4330 2016-01-05   14.0 2016-01-07  price"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shift.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "`df_shift` is ready to be passed into the feature extraction process in tsfresh "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 251/251 [00:09<00:00, 26.65it/s]\n",
      "WARNING:tsfresh.utilities.dataframe_functions:The columns ['feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"stderr\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"intercept\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"rvalue\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"slope\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"mean\"__chunk_len_50__attr_\"stderr\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"intercept\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"rvalue\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"slope\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"min\"__chunk_len_50__attr_\"stderr\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"intercept\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"rvalue\"'\n",
      " 'feature__agg_linear_trend__f_agg_\"var\"__chunk_len_50__attr_\"slope\"'\n",
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      " 'feature__fft_coefficient__coeff_88__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_88__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_89__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_89__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_89__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_89__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_90__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_90__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_90__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_90__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_91__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_91__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_91__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_91__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_92__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_92__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_92__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_92__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_93__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_93__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_93__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_93__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_94__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_94__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_94__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_94__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_95__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_95__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_95__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_95__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_96__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_96__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_96__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_96__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_97__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_97__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_97__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_97__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_98__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_98__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_98__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_98__attr_\"real\"'\n",
      " 'feature__fft_coefficient__coeff_99__attr_\"abs\"'\n",
      " 'feature__fft_coefficient__coeff_99__attr_\"angle\"'\n",
      " 'feature__fft_coefficient__coeff_99__attr_\"imag\"'\n",
      " 'feature__fft_coefficient__coeff_99__attr_\"real\"'] did not have any finite values. Filling with zeros.\n"
     ]
    }
   ],
   "source": [
    "X = extract_features(df_shift, column_id=\"id\", column_sort=\"time\", column_value=\"value\", impute_function=impute,\n",
    "                     show_warnings=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(251, 788)\n",
      "(251, 341)\n"
     ]
    }
   ],
   "source": [
    "# drop constant features\n",
    "print(X.shape)\n",
    "X = X.loc[:, X.apply(pd.Series.nunique) != 1] \n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add last value as feature\n",
    "X[\"feature_last_value\"] = y.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Drop first line\n",
    "X = X.iloc[1:, ]\n",
    "y = y.iloc[1: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>variable</th>\n",
       "      <th>feature__abs_energy</th>\n",
       "      <th>feature__absolute_sum_of_changes</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"mean\"</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"median\"</th>\n",
       "      <th>feature__agg_autocorrelation__f_agg_\"var\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\"</th>\n",
       "      <th>feature__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\"</th>\n",
       "      <th>...</th>\n",
       "      <th>feature__symmetry_looking__r_0.75</th>\n",
       "      <th>feature__symmetry_looking__r_0.8</th>\n",
       "      <th>feature__symmetry_looking__r_0.85</th>\n",
       "      <th>feature__symmetry_looking__r_0.9</th>\n",
       "      <th>feature__symmetry_looking__r_0.95</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_1</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_2</th>\n",
       "      <th>feature__time_reversal_asymmetry_statistic__lag_3</th>\n",
       "      <th>feature__variance</th>\n",
       "      <th>feature_last_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>392.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>13.0015</td>\n",
       "      <td>-0.219347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>575.8736</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.625000</td>\n",
       "      <td>-0.625000</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>13.0015</td>\n",
       "      <td>-0.219347</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-169.769600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043022</td>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>745.9152</td>\n",
       "      <td>0.96</td>\n",
       "      <td>-0.612853</td>\n",
       "      <td>-0.783749</td>\n",
       "      <td>0.483390</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>13.0015</td>\n",
       "      <td>-0.219347</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-260.882752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156300</td>\n",
       "      <td>13.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>917.5252</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-0.558454</td>\n",
       "      <td>-0.804121</td>\n",
       "      <td>0.562130</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>13.0015</td>\n",
       "      <td>-0.219347</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-227.227616</td>\n",
       "      <td>-330.728400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.173440</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1081.8776</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-0.583313</td>\n",
       "      <td>-0.754310</td>\n",
       "      <td>0.672709</td>\n",
       "      <td>13.33</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-189.052842</td>\n",
       "      <td>-371.706552</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216533</td>\n",
       "      <td>12.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 342 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variable    feature__abs_energy  feature__absolute_sum_of_changes  \\\n",
       "id                                                                  \n",
       "2016-01-06             392.0000                              0.00   \n",
       "2016-01-07             575.8736                              0.44   \n",
       "2016-01-08             745.9152                              0.96   \n",
       "2016-01-11             917.5252                              1.02   \n",
       "2016-01-12            1081.8776                              1.30   \n",
       "\n",
       "variable    feature__agg_autocorrelation__f_agg_\"mean\"  \\\n",
       "id                                                       \n",
       "2016-01-06                                    0.000000   \n",
       "2016-01-07                                   -0.625000   \n",
       "2016-01-08                                   -0.612853   \n",
       "2016-01-11                                   -0.558454   \n",
       "2016-01-12                                   -0.583313   \n",
       "\n",
       "variable    feature__agg_autocorrelation__f_agg_\"median\"  \\\n",
       "id                                                         \n",
       "2016-01-06                                      0.000000   \n",
       "2016-01-07                                     -0.625000   \n",
       "2016-01-08                                     -0.783749   \n",
       "2016-01-11                                     -0.804121   \n",
       "2016-01-12                                     -0.754310   \n",
       "\n",
       "variable    feature__agg_autocorrelation__f_agg_\"var\"  \\\n",
       "id                                                      \n",
       "2016-01-06                                   0.000000   \n",
       "2016-01-07                                   0.140625   \n",
       "2016-01-08                                   0.483390   \n",
       "2016-01-11                                   0.562130   \n",
       "2016-01-12                                   0.672709   \n",
       "\n",
       "variable    feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"intercept\"  \\\n",
       "id                                                                                   \n",
       "2016-01-06                                              13.33                        \n",
       "2016-01-07                                              13.33                        \n",
       "2016-01-08                                              13.33                        \n",
       "2016-01-11                                              13.33                        \n",
       "2016-01-12                                              13.33                        \n",
       "\n",
       "variable    feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"rvalue\"  \\\n",
       "id                                                                                \n",
       "2016-01-06                                               -1.0                     \n",
       "2016-01-07                                               -1.0                     \n",
       "2016-01-08                                               -1.0                     \n",
       "2016-01-11                                               -1.0                     \n",
       "2016-01-12                                               -1.0                     \n",
       "\n",
       "variable    feature__agg_linear_trend__f_agg_\"max\"__chunk_len_10__attr_\"slope\"  \\\n",
       "id                                                                               \n",
       "2016-01-06                                              -0.01                    \n",
       "2016-01-07                                              -0.01                    \n",
       "2016-01-08                                              -0.01                    \n",
       "2016-01-11                                              -0.01                    \n",
       "2016-01-12                                              -0.01                    \n",
       "\n",
       "variable    feature__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"intercept\"  \\\n",
       "id                                                                                  \n",
       "2016-01-06                                            13.0015                       \n",
       "2016-01-07                                            13.0015                       \n",
       "2016-01-08                                            13.0015                       \n",
       "2016-01-11                                            13.0015                       \n",
       "2016-01-12                                            14.0000                       \n",
       "\n",
       "variable    feature__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"rvalue\"  \\\n",
       "id                                                                               \n",
       "2016-01-06                                          -0.219347                    \n",
       "2016-01-07                                          -0.219347                    \n",
       "2016-01-08                                          -0.219347                    \n",
       "2016-01-11                                          -0.219347                    \n",
       "2016-01-12                                          -1.000000                    \n",
       "\n",
       "variable           ...          feature__symmetry_looking__r_0.75  \\\n",
       "id                 ...                                              \n",
       "2016-01-06         ...                                        0.0   \n",
       "2016-01-07         ...                                        1.0   \n",
       "2016-01-08         ...                                        1.0   \n",
       "2016-01-11         ...                                        1.0   \n",
       "2016-01-12         ...                                        1.0   \n",
       "\n",
       "variable    feature__symmetry_looking__r_0.8  \\\n",
       "id                                             \n",
       "2016-01-06                               0.0   \n",
       "2016-01-07                               1.0   \n",
       "2016-01-08                               1.0   \n",
       "2016-01-11                               1.0   \n",
       "2016-01-12                               1.0   \n",
       "\n",
       "variable    feature__symmetry_looking__r_0.85  \\\n",
       "id                                              \n",
       "2016-01-06                                0.0   \n",
       "2016-01-07                                1.0   \n",
       "2016-01-08                                1.0   \n",
       "2016-01-11                                1.0   \n",
       "2016-01-12                                1.0   \n",
       "\n",
       "variable    feature__symmetry_looking__r_0.9  \\\n",
       "id                                             \n",
       "2016-01-06                               0.0   \n",
       "2016-01-07                               1.0   \n",
       "2016-01-08                               1.0   \n",
       "2016-01-11                               1.0   \n",
       "2016-01-12                               1.0   \n",
       "\n",
       "variable    feature__symmetry_looking__r_0.95  \\\n",
       "id                                              \n",
       "2016-01-06                                0.0   \n",
       "2016-01-07                                1.0   \n",
       "2016-01-08                                1.0   \n",
       "2016-01-11                                1.0   \n",
       "2016-01-12                                1.0   \n",
       "\n",
       "variable    feature__time_reversal_asymmetry_statistic__lag_1  \\\n",
       "id                                                              \n",
       "2016-01-06                                           0.000000   \n",
       "2016-01-07                                        -169.769600   \n",
       "2016-01-08                                        -260.882752   \n",
       "2016-01-11                                        -227.227616   \n",
       "2016-01-12                                        -189.052842   \n",
       "\n",
       "variable    feature__time_reversal_asymmetry_statistic__lag_2  \\\n",
       "id                                                              \n",
       "2016-01-06                                           0.000000   \n",
       "2016-01-07                                           0.000000   \n",
       "2016-01-08                                           0.000000   \n",
       "2016-01-11                                        -330.728400   \n",
       "2016-01-12                                        -371.706552   \n",
       "\n",
       "variable    feature__time_reversal_asymmetry_statistic__lag_3  \\\n",
       "id                                                              \n",
       "2016-01-06                                                0.0   \n",
       "2016-01-07                                                0.0   \n",
       "2016-01-08                                                0.0   \n",
       "2016-01-11                                                0.0   \n",
       "2016-01-12                                                0.0   \n",
       "\n",
       "variable    feature__variance  feature_last_value  \n",
       "id                                                 \n",
       "2016-01-06           0.000000               14.00  \n",
       "2016-01-07           0.043022               13.56  \n",
       "2016-01-08           0.156300               13.04  \n",
       "2016-01-11           0.173440               13.10  \n",
       "2016-01-12           0.216533               12.82  \n",
       "\n",
       "[5 rows x 342 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#  Fit Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 150/150 [00:14<00:00,  8.20it/s]\n"
     ]
    }
   ],
   "source": [
    "ada = AdaBoostRegressor(n_estimators=10)\n",
    "y_pred = [np.NaN] * len(y)\n",
    "\n",
    "isp = 100   # index of where to start the predictions\n",
    "assert isp > 0\n",
    "\n",
    "for i in tqdm(range(isp, len(y))):\n",
    "    \n",
    "    ada.fit(X.iloc[:i], y[:i])\n",
    "    y_pred[i] = ada.predict(X.iloc[i, :])[0]\n",
    "    \n",
    "y_pred = pd.Series(data=y_pred, index=y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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soA0M2oiIWgGDNmpbyVKDNp81iESp+jmtTFs5PW09Hebzt8PY/3J62mphI+9q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E8yh20hyb7X1NwCpsXNuRclnTlnizG2gZNJmSvOwUCbH7fuuArPz/wHXp44AwBILZqYTm7u/VMC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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fb5690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(y, label=\"true\")\n",
    "plt.plot(y_pred, label=\"ada\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will also inspect last value before the prediction as a benchmark tool, denoted by y-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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80VFOjxa58+aN9Gwo8PkDX+KPHvkMPz37KD3JPL9yzb/nrTvfSF+3Cm26q6pN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\nHBV+2n1dW9weGehqEEmr2iMNXSMIQY9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zNbYxBrtZxtPdcFjO0B6GYaaBwwFON3COp4HT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\nobu70IBXboqZiPOuEOeOsQDAm8+dYyB7kredfMu2wzuk0yYDNrJStO1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FAAAg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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11620e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(y, label=\"true\")\n",
    "plt.plot(y.shift(1), label=\"y-1\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looks not too bad. The green curve is the output of the AdaBoost Regressor, the blue curve is the true High value.\n",
    "\n",
    "We can also inspect the relevance of the extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE y-1: \t0.1192\n",
      "MAE ada: \t0.166252822898\n"
     ]
    }
   ],
   "source": [
    "print(\"MAE y-1: \\t{}\".format(np.mean(np.abs(np.diff(y))[isp-1:] )))\n",
    "print(\"MAE ada: \\t{}\".format(np.mean(np.abs(y_pred - y)[isp:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we are not yet beating the y-1 benchmark, so we need to invest more time into building dedicated features or use a better model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "variable\n",
       "feature_last_value                                                      0.881837\n",
       "feature__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8     0.013681\n",
       "feature__agg_linear_trend__f_agg_\"max\"__chunk_len_5__attr_\"stderr\"      0.010057\n",
       "feature__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_11__w_10        0.007405\n",
       "feature__last_location_of_maximum                                       0.006794\n",
       "feature__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.2__ql_0.0    0.005930\n",
       "feature__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_7__w_20         0.005903\n",
       "feature__energy_ratio_by_chunks__num_segments_10__segment_focus_6       0.005718\n",
       "feature__agg_linear_trend__f_agg_\"var\"__chunk_len_5__attr_\"stderr\"      0.005650\n",
       "feature__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.6__ql_0.2     0.004687\n",
       "feature__change_quantiles__f_agg_\"mean\"__isabs_False__qh_1.0__ql_0.8    0.004465\n",
       "feature__autocorrelation__lag_9                                         0.003935\n",
       "feature__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.2     0.003911\n",
       "feature__change_quantiles__f_agg_\"var\"__isabs_True__qh_0.2__ql_0.0      0.003770\n",
       "feature__sum_of_reoccurring_data_points                                 0.003454\n",
       "feature__autocorrelation__lag_7                                         0.003008\n",
       "feature__agg_linear_trend__f_agg_\"mean\"__chunk_len_5__attr_\"rvalue\"     0.002993\n",
       "feature__fft_coefficient__coeff_8__attr_\"abs\"                           0.002960\n",
       "feature__ratio_beyond_r_sigma__r_1.5                                    0.002601\n",
       "feature__fft_coefficient__coeff_7__attr_\"real\"                          0.002573\n",
       "feature__kurtosis                                                       0.002145\n",
       "feature__autocorrelation__lag_1                                         0.002029\n",
       "feature__fft_coefficient__coeff_9__attr_\"real\"                          0.001788\n",
       "feature__change_quantiles__f_agg_\"mean\"__isabs_True__qh_0.4__ql_0.2     0.001741\n",
       "feature__autocorrelation__lag_6                                         0.001500\n",
       "feature__ar_coefficient__k_10__coeff_3                                  0.001476\n",
       "feature__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_8__w_20         0.001384\n",
       "feature__fft_coefficient__coeff_2__attr_\"real\"                          0.001337\n",
       "feature__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_2          0.001094\n",
       "feature__change_quantiles__f_agg_\"var\"__isabs_False__qh_0.4__ql_0.2     0.001059\n",
       "                                                                          ...   \n",
       "feature__index_mass_quantile__q_0.2                                     0.000000\n",
       "feature__index_mass_quantile__q_0.3                                     0.000000\n",
       "feature__index_mass_quantile__q_0.4                                     0.000000\n",
       "feature__index_mass_quantile__q_0.6                                     0.000000\n",
       "feature__index_mass_quantile__q_0.7                                     0.000000\n",
       "feature__index_mass_quantile__q_0.8                                     0.000000\n",
       "feature__index_mass_quantile__q_0.9                                     0.000000\n",
       "feature__large_standard_deviation__r_0.05                               0.000000\n",
       "feature__large_standard_deviation__r_0.1                                0.000000\n",
       "feature__first_location_of_minimum                                      0.000000\n",
       "feature__first_location_of_maximum                                      0.000000\n",
       "feature__fft_coefficient__coeff_9__attr_\"imag\"                          0.000000\n",
       "feature__fft_coefficient__coeff_6__attr_\"imag\"                          0.000000\n",
       "feature__fft_coefficient__coeff_4__attr_\"real\"                          0.000000\n",
       "feature__fft_coefficient__coeff_5__attr_\"abs\"                           0.000000\n",
       "feature__fft_coefficient__coeff_5__attr_\"angle\"                         0.000000\n",
       "feature__fft_coefficient__coeff_5__attr_\"imag\"                          0.000000\n",
       "feature__fft_coefficient__coeff_5__attr_\"real\"                          0.000000\n",
       "feature__fft_coefficient__coeff_6__attr_\"abs\"                           0.000000\n",
       "feature__fft_coefficient__coeff_6__attr_\"angle\"                         0.000000\n",
       "feature__fft_coefficient__coeff_6__attr_\"real\"                          0.000000\n",
       "feature__fft_coefficient__coeff_9__attr_\"angle\"                         0.000000\n",
       "feature__fft_coefficient__coeff_7__attr_\"abs\"                           0.000000\n",
       "feature__fft_coefficient__coeff_7__attr_\"angle\"                         0.000000\n",
       "feature__fft_coefficient__coeff_7__attr_\"imag\"                          0.000000\n",
       "feature__fft_coefficient__coeff_8__attr_\"angle\"                         0.000000\n",
       "feature__fft_coefficient__coeff_8__attr_\"imag\"                          0.000000\n",
       "feature__fft_coefficient__coeff_8__attr_\"real\"                          0.000000\n",
       "feature__fft_coefficient__coeff_9__attr_\"abs\"                           0.000000\n",
       "feature__abs_energy                                                     0.000000\n",
       "Length: 342, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.Series(index=X.columns, data=ada.feature_importances_)\n",
    "importances.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "So, the minumum value \"feature__maximum\" during the last 10 values had the highest importance to predict the next value of the `High` column"
   ]
  }
 ],
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